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   "source": [
    "# Tutorial 1 - How to run a model"
   ]
  },
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   "source": [
    "Welcome to PyBaMM! In this notebook, we will run your first PyBaMM model in just a few simple lines. \n",
    "\n",
    "The first we need to do is to import PyBaMM, so we can use its capabilities. Note that the very first line is only needed in Google Colab."
   ]
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     "name": "stdout",
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     "text": [
      "\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.3.2\u001B[0m\n",
      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    }
   ],
   "source": [
    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
    "import pybamm"
   ]
  },
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   "source": [
    "First, we load the model that we wish to run from PyBaMM's model library. For this notebook, we choose the Doyle-Fuller-Newman (DFN) model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.lithium_ion.DFN()"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "We now use this model to create a PyBaMM `Simulation`, which is used to process and solve the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = pybamm.Simulation(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then call `solve` on our simulation object to solve the model, passing the window of time to solve for in seconds (here 1 hour):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pybamm.solvers.solution.Solution at 0x7fbf9d8accd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.solve([0, 3600])"
   ]
  },
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   "source": [
    "Once the simulation is solved, we can call `plot` to generate a dynamic plot of the key variables:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4442a3198774c4eb5e826e6fb14c58c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7fbf9d6f9f50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we have solved a model with the inbuilt default settings. However, PyBaMM is designed to be highly customisable. Over the course of the getting started tutorials, we will see how various settings can be changed so that the model is appropriate for your situation. In [Tutorial 2](./tutorial-2-compare-models.ipynb) we cover how to simulate and compare different models."
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "If you write a paper that uses PyBaMM, we would be grateful if you could cite the papers relevant to your code. These will change depending on what models and solvers you use. To find out which papers you should cite, you can run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pybamm.print_citations()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can print the citations in BibTeX format by running\n",
    "\n",
    "```python3\n",
    "pybamm.print_citations(output_format=\"bibtex\")\n",
    "```\n",
    "\n",
    "In both cases, you can pass the extra argument `filename` to store the citations into a file."
   ]
  },
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   "source": []
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